Autonomic cloud resource provisioning and scheduling using meta-heuristic algorithm

Kumar, Mithilesh; Sharma, S. C.; Goel, Shalini; Mishra, Sambit Kumar; Husain, Akhtar

doi:10.1007/s00521-020-04955-y

Cited by 38 publications

(10 citation statements)

References 33 publications

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“…The simulation outcomes show that the suggested hybrid algorithm may allocate the services more effectively. The computational results in [55] show that the improved binary PSO (BPSO) algorithm, which is based on a transfer function, is more effective in optimizing several quality-of-service metrics, such as makespan time, energy consumption, and execution cost. Goyal and Kant [46] devised a new hybrid algorithm for protecting cloud data and used it in practice.…”

Section: Literature Reviewmentioning

confidence: 99%

Cloud data security and various cryptographic algorithms

Alemami

Al-Ghonmein

Al-Moghrabi

et al. 2023

IJECE

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<p>Cloud computing has spread widely among different organizations due to its advantages, such as cost reduction, resource pooling, broad network access, and ease of administration. It increases the abilities of physical resources by optimizing shared use. Clients’ valuable items (data and applications) are moved outside of regulatory supervision in a shared environment where many clients are grouped together. However, this process poses security concerns, such as sensitive information theft and personally identifiable data leakage. Many researchers have contributed to reducing the problem of data security in cloud computing by developing a variety of technologies to secure cloud data, including encryption. In this study, a set of encryption algorithms (advance encryption standard (AES), data encryption standard (DES), Blowfish, Rivest-Shamir-Adleman (RSA) encryption, and international data encryption algorithm (IDEA) was compared in terms of security, data encipherment capacity, memory usage, and encipherment time to determine the optimal algorithm for securing cloud information from hackers. Results show that RSA and IDEA are less secure than AES, Blowfish, and DES). The AES algorithm encrypts a huge amount of data, takes the least encipherment time, and is faster than other algorithms, and the Blowfish algorithm requires the least amount of memory space.</p>

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Section: Literature Reviewmentioning

confidence: 99%

Cloud data security and various cryptographic algorithms

Alemami

Al-Ghonmein

Al-Moghrabi

et al. 2023

IJECE

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“…Authors in [19] proposed an approach to perform cloud resource provisioning and scheduling based on metaheuristic algorithms. To design the supporting model for the autonomic resource that schedules the applications effectively, the binary PSO (BPSO) algorithm is used.…”

Section: Related Work and Limitations Of Used Algorithms And Processesmentioning

confidence: 99%

Limitations of Load Balancing and Performance Analysis Processes and Algorithms in Cloud Computing

Kanbar¹,

Faraj²

2023

UHD J SCI TECH

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In the modern IT industry, cloud computing is a cutting-edge technology. Since it faces various challenges, the most significant problem of cloud computing is load balancing, which degrades the performance of the computing resources. In earlier research studies, the management of the workload to address all resource allocation challenges that caused by the participation of a large number of users has received important attention. When several people are attempting to access a given web application at once, managing all of those users becomes exceedingly difficult. One of the elements affecting the performance stability of cloud computing is load balancing. This article evaluates and discusses load balancing, the drawbacks of the numerous methods that have been suggested to distribute load among nodes, and the variables that are taken into account when determining the best load balancing algorithm.

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“…Additionally, a novel modified particle swarm optimization algorithm has been developed to efficiently schedule the tasks on resources. Kumar et al 30 The autonomic resource allocation techniques in the existing literature, whose comparison is given in Table 1, do not change their operational behavior in accordance with the variation in workload. They support only single mode of operation, depicted by S in the table.…”

Section: Literature Reviewmentioning

confidence: 99%

An autonomic resource management system for energy efficient and quality of service aware resource scheduling in cloud environment

Kumar

Lal

Kaur

2023

Concurrency and Computation

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SummaryEfficient utilization of resources is a challenging task in cloud computing due to heterogeneity of resources and fluctuating resource demands of multifarious applications. This article presents an autonomic resource allocation approach for energy efficient utilization of resources while satisfying QoS needs of the end‐users. The proposed approach is based on antlion optimization and makes use of resource utilization history for autonomic allocation and efficient use of resources. It can operate in one of the three different modes namely, energy‐saving, performance, and balanced mode depending on the projected workload of an application. The performance analysis of the proposed approach is carried out in CloudSim simulator under different workload conditions.

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Autonomic cloud resource provisioning and scheduling using meta-heuristic algorithm

Cited by 38 publications

References 33 publications

Cloud data security and various cryptographic algorithms

Cloud data security and various cryptographic algorithms

Limitations of Load Balancing and Performance Analysis Processes and Algorithms in Cloud Computing

An autonomic resource management system for energy efficient and quality of service aware resource scheduling in cloud environment

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